This paper describes research into a high speed image processing system using parallel digital signal processors for the processing of electro-optic images. The objective of the system is to reduce the processing time...
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ISBN:
(纸本)0819416207
This paper describes research into a high speed image processing system using parallel digital signal processors for the processing of electro-optic images. The objective of the system is to reduce the processing time of non-contact type inspection problems including industrial and medical applications. A single processor can not deliver sufficient processing power required for the use of applications hence, a MIMD system is designed and constructed to enable fast processing of electro-optic images. The Texas Instruments TMS320C40 digital signal processor is used due to its high speed floating point CPU and the support for the parallel processing environment. A custom designed VISION bus is provided to transfer images between processors. The system is being applied for solder joint inspection of high technology printed circuit boards.
We consider the relationship between modelling dynamics with a nonlinear function approximation and the application of a noise reduction algorithm. Filtering the data with such an algorithm is then shown to provide a ...
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ISBN:
(纸本)0819416207
We consider the relationship between modelling dynamics with a nonlinear function approximation and the application of a noise reduction algorithm. Filtering the data with such an algorithm is then shown to provide a better deterministic model for the data than an ordinary least squares estimate.
A two-sided (or complete) orthogonal decomposition of an m × n matrix A is a product of an orthogonal matrix, a triangular matrix, and another orthogonal matrix. Two examples are the URV and ULV decompositions. I...
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ISBN:
(纸本)0819416207
A two-sided (or complete) orthogonal decomposition of an m × n matrix A is a product of an orthogonal matrix, a triangular matrix, and another orthogonal matrix. Two examples are the URV and ULV decompositions. In this paper we present and analyze URV and ULV algorithms that are efficient whenever the numerical rank k of the matrix is much less than min(m,n). We also prove that good estimates of the singular vectors, needed in the algorithms, lead to good approximations of the singular subspaces of A.
A number of adaptive condition number estimators have been proposed in the past to dynamically estimate the sensitivity of the coefficient matrix of a linear systems of equations. Applications of these techniques ofte...
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ISBN:
(纸本)0819416207
A number of adaptive condition number estimators have been proposed in the past to dynamically estimate the sensitivity of the coefficient matrix of a linear systems of equations. Applications of these techniques often arise in the context of signalprocessing, where the information matrix is being updated with rank-one modifications. Various schemes, such as ACE, ALE and ICE, were proposed to cope with this problem. In this paper, we will briefly review the past work, and show how the small-sample condition estimator can be used in an adaptive manner.
The concern here is of retrieving damped harmonics and polynomial phase signals in the presence of additive noise. The damping function is not limited to the exponential model, and in certain cases, the additive noise...
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ISBN:
(纸本)0819416207
The concern here is of retrieving damped harmonics and polynomial phase signals in the presence of additive noise. The damping function is not limited to the exponential model, and in certain cases, the additive noise does not have to be white. Three classes of algorithms are presented, namely DFT based, Kumaresan-Tufts type extensions, and subspace variants including the MUSIC algorithm. Preference should be based on the available data length and frequency separations. In addition, retrieval of self coupled damped harmonics, which may be present when nonlinearities exist in physical systems, is investigated. Simulation examples illustrate main points of the paper.
The Rapid Prototyping of Application Specific signal Processors (RASSP) program has as its goal the dramatic improvement of the process by which signal processors are specified, designed, documented, manufactured, and...
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ISBN:
(纸本)0819416207
The Rapid Prototyping of Application Specific signal Processors (RASSP) program has as its goal the dramatic improvement of the process by which signal processors are specified, designed, documented, manufactured, and supported. A key part of the RASSP process is the method of architecture selection. An architecture overview is presented, together with a discussion of architecture classification, architecture evaluation factors, and signalprocessing flow. Two important architecture support aspects are briefly discussed: open systems architecture and integrated diagnostics. Recommendations for preferred approaches and rules of thumb for architecture selection are given. Finally, the RASSP architecture selection process is outlined, including a discussion of a standard hardware/software module interface.
We present an algorithm-based fault tolerant scheme for QR decomposition, that extends the well-known Gentleman-Kung-McWhirter triangular systolic array architecture. Assuming that the array is subject to transient fa...
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ISBN:
(纸本)0819416207
We present an algorithm-based fault tolerant scheme for QR decomposition, that extends the well-known Gentleman-Kung-McWhirter triangular systolic array architecture. Assuming that the array is subject to transient faults, widely separated in time and each affecting a single processor, we give an algorithm that corrects the full triangular array with computational overhead equivalent, on average, to the interpolation of a single extra vector into the data stream.
A massively parallel signal and image processing architecture is considered. The architecture is comprised of 2D arrays of cells that simulate the response of retina neurons. The results of simulations are compared to...
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ISBN:
(纸本)0819416207
A massively parallel signal and image processing architecture is considered. The architecture is comprised of 2D arrays of cells that simulate the response of retina neurons. The results of simulations are compared to previously published experimental results and the system is applied to detection of spatio-temporal features in sequences of images representative of pulse- doppler radar images. By arranging the output layer so that the cells respond to various key input features an array of feature extraction cells can be obtained. The system is characterized by developing an image space to feature space mapping.
Much research has focused on estimating the parameters of a sum of exponentially damped sinusoids. In some applications, such as nuclear magnetic resonance signalprocessing, only a few of the sinusoids are of interes...
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ISBN:
(纸本)0819416207
Much research has focused on estimating the parameters of a sum of exponentially damped sinusoids. In some applications, such as nuclear magnetic resonance signalprocessing, only a few of the sinusoids are of interest. This paper presents some new frequency-selective techniques for estimating the parameters of sinusoids within a specified frequency region using a subspace and SVD-based estimation algorithm and an FIR filter matrix. The applicable estimation algorithms in this technique are the linear prediction method, the matrix pencil method, Kung et al.'s method and its total-least-squares variant, called the HTLS method. The benefits of the frequency-selective technique combined with the HTLS estimation method are confirmed through simulations.
We propose a scale-limited signal model based on wavelet representation and study the reconstructability of scale-limited signals via extrapolation in this research. In analogy with the band-limited case, we define a ...
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ISBN:
(纸本)0819416207
We propose a scale-limited signal model based on wavelet representation and study the reconstructability of scale-limited signals via extrapolation in this research. In analogy with the band-limited case, we define a scale-limited time-concentrated operator, and examine various vector spaces associated with such an operator. It is proved that the scale-limited signal space can be decomposed into the direct sum of two subspaces and only the component in one subspace can be exactly reconstructed, where the reconstructable subspace can be interpreted as a space consisting of scale/time-limited signals. Due to the ill-posedness of scale-limited extrapolation, a regularization process is introduced for noisy data extrapolation.
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